{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb846718-6b9a-4b90-ae59-e4d464081a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据预处理完成，准备进行模型训练和测试。\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('./combined_data.csv')\n",
    "\n",
    "# 删除不需要的列，例如时间戳或IP地址（假设你的数据集中有这些列）\n",
    "data.drop([' Timestamp'], axis=1, inplace=True)\n",
    "\n",
    "# 类型转换，将分类标签编码\n",
    "label_encoder = LabelEncoder()\n",
    "data[' Label'] = label_encoder.fit_transform(data[' Label'])\n",
    "\n",
    "# 检查并处理无穷大和非常大的数值\n",
    "data.replace([np.inf, -np.inf], np.nan, inplace=True)  # 将inf替换为NaN\n",
    "data.fillna(data.median(), inplace=True)  # 使用中位数填充NaN，确保之前中位数计算不包括inf\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(data.drop(' Label', axis=1))  # 确保标签列不参与标准化\n",
    "y = data[' Label']\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "print(\"数据预处理完成，准备进行模型训练和测试。\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e37ed3d0-1d91-4076-9dfe-29aedfbe9983",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 转换数据为torch张量\n",
    "X_train_tensor = torch.tensor(X_train.astype(np.float32))  # 确保数据类型为 float32\n",
    "y_train_tensor = torch.tensor(y_train.values.astype(np.int64))  # 先获取values再转换类型为 long (int64)\n",
    "X_test_tensor = torch.tensor(X_test.astype(np.float32))\n",
    "y_test_tensor = torch.tensor(y_test.values.astype(np.int64))\n",
    "\n",
    "# 创建数据加载器\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6212401c-3fda-4324-94b2-9abaf3f9aa47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# 定义模型\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, input_size, num_classes):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=1, padding=1)\n",
    "        # 计算池化后的尺寸\n",
    "        conv1_out_size = (input_size + 2 * 1 - 3) / 1 + 1  # Conv1\n",
    "        pool1_out_size = conv1_out_size / 2  # Pool1\n",
    "        conv2_out_size = (pool1_out_size + 2 * 1 - 3) / 1 + 1  # Conv2\n",
    "        pool2_out_size = conv2_out_size / 2  # Pool2\n",
    "        final_size = int(pool2_out_size) * 32  # conv2 的输出通道数 * 输出长度\n",
    "        self.fc = nn.Linear(final_size, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.unsqueeze(1)  # Adding a channel dimension\n",
    "        x = self.relu(self.conv1(x))\n",
    "        x = self.pool(x)\n",
    "        x = self.relu(self.conv2(x))\n",
    "        x = self.pool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "# 初始化模型\n",
    "input_size = X_train.shape[1]\n",
    "num_classes = len(np.unique(y))\n",
    "model = CNN(input_size,num_classes)\n",
    "\n",
    "# 损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc9a2783-0daf-439f-9156-e1b28e8bbf7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/200], Loss: 1.3973, Train Acc: 34.84%, Test Acc: 43.33%\n",
      "Epoch [2/200], Loss: 1.2206, Train Acc: 45.16%, Test Acc: 47.08%\n",
      "Epoch [3/200], Loss: 1.6352, Train Acc: 47.86%, Test Acc: 51.00%\n",
      "Epoch [4/200], Loss: 1.1604, Train Acc: 50.05%, Test Acc: 51.38%\n",
      "Epoch [5/200], Loss: 1.1719, Train Acc: 49.98%, Test Acc: 52.79%\n",
      "Epoch [6/200], Loss: 0.8516, Train Acc: 52.07%, Test Acc: 51.75%\n",
      "Epoch [7/200], Loss: 1.1699, Train Acc: 52.05%, Test Acc: 49.71%\n",
      "Epoch [8/200], Loss: 1.0174, Train Acc: 52.91%, Test Acc: 51.83%\n",
      "Epoch [9/200], Loss: 1.0593, Train Acc: 52.18%, Test Acc: 53.83%\n",
      "Epoch [10/200], Loss: 0.8039, Train Acc: 53.02%, Test Acc: 53.71%\n",
      "Epoch [11/200], Loss: 1.1531, Train Acc: 53.21%, Test Acc: 53.25%\n",
      "Epoch [12/200], Loss: 0.9476, Train Acc: 52.73%, Test Acc: 53.83%\n",
      "Epoch [13/200], Loss: 0.9459, Train Acc: 54.20%, Test Acc: 51.38%\n",
      "Epoch [14/200], Loss: 0.9058, Train Acc: 53.93%, Test Acc: 53.46%\n",
      "Epoch [15/200], Loss: 1.0855, Train Acc: 55.46%, Test Acc: 53.54%\n",
      "Epoch [16/200], Loss: 0.9583, Train Acc: 54.05%, Test Acc: 54.25%\n",
      "Epoch [17/200], Loss: 0.8812, Train Acc: 54.54%, Test Acc: 55.25%\n",
      "Epoch [18/200], Loss: 0.7947, Train Acc: 54.93%, Test Acc: 53.42%\n",
      "Epoch [19/200], Loss: 0.9942, Train Acc: 54.89%, Test Acc: 56.04%\n",
      "Epoch [20/200], Loss: 0.7749, Train Acc: 55.68%, Test Acc: 53.08%\n",
      "Epoch [21/200], Loss: 1.1074, Train Acc: 55.50%, Test Acc: 54.04%\n",
      "Epoch [22/200], Loss: 1.0857, Train Acc: 55.30%, Test Acc: 55.83%\n",
      "Epoch [23/200], Loss: 0.8571, Train Acc: 55.36%, Test Acc: 55.21%\n",
      "Epoch [24/200], Loss: 1.0767, Train Acc: 56.59%, Test Acc: 54.29%\n",
      "Epoch [25/200], Loss: 0.9371, Train Acc: 56.02%, Test Acc: 55.50%\n",
      "Epoch [26/200], Loss: 0.8942, Train Acc: 56.59%, Test Acc: 53.92%\n",
      "Epoch [27/200], Loss: 1.1046, Train Acc: 57.21%, Test Acc: 56.12%\n",
      "Epoch [28/200], Loss: 0.9320, Train Acc: 58.23%, Test Acc: 55.62%\n",
      "Epoch [29/200], Loss: 0.8858, Train Acc: 57.29%, Test Acc: 56.21%\n",
      "Epoch [30/200], Loss: 1.0122, Train Acc: 56.89%, Test Acc: 55.88%\n",
      "Epoch [31/200], Loss: 1.0049, Train Acc: 57.79%, Test Acc: 55.08%\n",
      "Epoch [32/200], Loss: 0.9550, Train Acc: 57.59%, Test Acc: 57.21%\n",
      "Epoch [33/200], Loss: 0.8006, Train Acc: 57.73%, Test Acc: 58.96%\n",
      "Epoch [34/200], Loss: 1.0634, Train Acc: 57.89%, Test Acc: 57.25%\n",
      "Epoch [35/200], Loss: 0.8043, Train Acc: 58.32%, Test Acc: 57.62%\n",
      "Epoch [36/200], Loss: 0.9066, Train Acc: 57.55%, Test Acc: 56.92%\n",
      "Epoch [37/200], Loss: 0.9291, Train Acc: 58.64%, Test Acc: 58.21%\n",
      "Epoch [38/200], Loss: 0.7037, Train Acc: 58.18%, Test Acc: 57.38%\n",
      "Epoch [39/200], Loss: 0.8919, Train Acc: 58.39%, Test Acc: 59.12%\n",
      "Epoch [40/200], Loss: 0.7744, Train Acc: 57.89%, Test Acc: 58.17%\n",
      "Epoch [41/200], Loss: 0.6996, Train Acc: 58.30%, Test Acc: 56.42%\n",
      "Epoch [42/200], Loss: 0.7273, Train Acc: 59.57%, Test Acc: 58.58%\n",
      "Epoch [43/200], Loss: 0.7912, Train Acc: 60.09%, Test Acc: 58.88%\n",
      "Epoch [44/200], Loss: 0.7448, Train Acc: 59.46%, Test Acc: 58.08%\n",
      "Epoch [45/200], Loss: 0.9249, Train Acc: 58.38%, Test Acc: 58.08%\n",
      "Epoch [46/200], Loss: 1.0498, Train Acc: 58.88%, Test Acc: 58.21%\n",
      "Epoch [47/200], Loss: 0.7219, Train Acc: 59.48%, Test Acc: 59.29%\n",
      "Epoch [48/200], Loss: 0.7576, Train Acc: 59.66%, Test Acc: 58.79%\n",
      "Epoch [49/200], Loss: 0.8401, Train Acc: 59.98%, Test Acc: 57.58%\n",
      "Epoch [50/200], Loss: 0.7703, Train Acc: 59.62%, Test Acc: 58.21%\n",
      "Epoch [51/200], Loss: 0.9031, Train Acc: 59.64%, Test Acc: 59.38%\n",
      "Epoch [52/200], Loss: 1.0644, Train Acc: 59.75%, Test Acc: 60.04%\n",
      "Epoch [53/200], Loss: 0.9742, Train Acc: 59.89%, Test Acc: 58.38%\n",
      "Epoch [54/200], Loss: 1.0863, Train Acc: 59.29%, Test Acc: 59.50%\n",
      "Epoch [55/200], Loss: 0.7899, Train Acc: 59.11%, Test Acc: 59.67%\n",
      "Epoch [56/200], Loss: 0.7542, Train Acc: 59.73%, Test Acc: 58.67%\n",
      "Epoch [57/200], Loss: 0.6713, Train Acc: 59.54%, Test Acc: 59.29%\n",
      "Epoch [58/200], Loss: 0.7466, Train Acc: 59.71%, Test Acc: 59.83%\n",
      "Epoch [59/200], Loss: 0.7497, Train Acc: 60.05%, Test Acc: 59.04%\n",
      "Epoch [60/200], Loss: 0.7458, Train Acc: 60.73%, Test Acc: 59.38%\n",
      "Epoch [61/200], Loss: 0.8341, Train Acc: 59.86%, Test Acc: 58.38%\n",
      "Epoch [62/200], Loss: 0.7524, Train Acc: 60.02%, Test Acc: 59.83%\n",
      "Epoch [63/200], Loss: 0.6874, Train Acc: 61.00%, Test Acc: 59.83%\n",
      "Epoch [64/200], Loss: 0.6169, Train Acc: 59.64%, Test Acc: 59.83%\n",
      "Epoch [65/200], Loss: 1.1436, Train Acc: 60.27%, Test Acc: 60.38%\n",
      "Epoch [66/200], Loss: 0.7439, Train Acc: 59.52%, Test Acc: 58.96%\n",
      "Epoch [67/200], Loss: 0.7994, Train Acc: 60.05%, Test Acc: 59.54%\n",
      "Epoch [68/200], Loss: 0.6907, Train Acc: 60.48%, Test Acc: 60.42%\n",
      "Epoch [69/200], Loss: 0.9073, Train Acc: 60.43%, Test Acc: 59.67%\n",
      "Epoch [70/200], Loss: 0.6369, Train Acc: 60.09%, Test Acc: 59.08%\n",
      "Epoch [71/200], Loss: 0.6071, Train Acc: 60.27%, Test Acc: 58.04%\n",
      "Epoch [72/200], Loss: 0.8553, Train Acc: 59.93%, Test Acc: 58.83%\n",
      "Epoch [73/200], Loss: 0.6625, Train Acc: 61.02%, Test Acc: 60.71%\n",
      "Epoch [74/200], Loss: 0.7591, Train Acc: 60.66%, Test Acc: 60.17%\n",
      "Epoch [75/200], Loss: 0.7439, Train Acc: 60.96%, Test Acc: 58.12%\n",
      "Epoch [76/200], Loss: 0.7838, Train Acc: 60.59%, Test Acc: 57.50%\n",
      "Epoch [77/200], Loss: 0.8547, Train Acc: 59.96%, Test Acc: 59.96%\n",
      "Epoch [78/200], Loss: 0.7117, Train Acc: 60.11%, Test Acc: 60.46%\n",
      "Epoch [79/200], Loss: 0.7315, Train Acc: 60.41%, Test Acc: 60.00%\n",
      "Epoch [80/200], Loss: 0.7079, Train Acc: 60.39%, Test Acc: 59.25%\n",
      "Epoch [81/200], Loss: 1.1427, Train Acc: 61.05%, Test Acc: 60.29%\n",
      "Epoch [82/200], Loss: 0.7815, Train Acc: 61.57%, Test Acc: 60.67%\n",
      "Epoch [83/200], Loss: 0.7610, Train Acc: 60.64%, Test Acc: 58.75%\n",
      "Epoch [84/200], Loss: 0.9457, Train Acc: 60.46%, Test Acc: 60.12%\n",
      "Epoch [85/200], Loss: 0.8279, Train Acc: 60.55%, Test Acc: 59.75%\n",
      "Epoch [86/200], Loss: 0.9241, Train Acc: 60.96%, Test Acc: 60.21%\n",
      "Epoch [87/200], Loss: 0.5659, Train Acc: 59.95%, Test Acc: 60.62%\n",
      "Epoch [88/200], Loss: 0.7156, Train Acc: 60.27%, Test Acc: 58.79%\n",
      "Epoch [89/200], Loss: 0.5985, Train Acc: 61.11%, Test Acc: 60.00%\n",
      "Epoch [90/200], Loss: 0.9726, Train Acc: 60.21%, Test Acc: 60.33%\n",
      "Epoch [91/200], Loss: 0.8457, Train Acc: 60.18%, Test Acc: 59.58%\n",
      "Epoch [92/200], Loss: 0.6881, Train Acc: 60.66%, Test Acc: 59.92%\n",
      "Epoch [93/200], Loss: 0.7551, Train Acc: 60.54%, Test Acc: 60.38%\n",
      "Epoch [94/200], Loss: 0.7492, Train Acc: 60.36%, Test Acc: 59.83%\n",
      "Epoch [95/200], Loss: 0.8651, Train Acc: 60.57%, Test Acc: 59.33%\n",
      "Epoch [96/200], Loss: 0.8065, Train Acc: 60.21%, Test Acc: 58.17%\n",
      "Epoch [97/200], Loss: 1.0015, Train Acc: 60.73%, Test Acc: 58.33%\n",
      "Epoch [98/200], Loss: 0.7648, Train Acc: 60.86%, Test Acc: 58.62%\n",
      "Epoch [99/200], Loss: 0.8920, Train Acc: 60.57%, Test Acc: 59.96%\n",
      "Epoch [100/200], Loss: 0.6997, Train Acc: 61.57%, Test Acc: 60.12%\n",
      "Epoch [101/200], Loss: 0.7819, Train Acc: 61.04%, Test Acc: 59.83%\n",
      "Epoch [102/200], Loss: 0.7770, Train Acc: 61.61%, Test Acc: 60.17%\n",
      "Epoch [103/200], Loss: 0.7872, Train Acc: 60.82%, Test Acc: 59.83%\n",
      "Epoch [104/200], Loss: 0.8212, Train Acc: 61.18%, Test Acc: 60.62%\n",
      "Epoch [105/200], Loss: 0.7323, Train Acc: 60.38%, Test Acc: 57.96%\n",
      "Epoch [106/200], Loss: 0.9663, Train Acc: 61.36%, Test Acc: 59.38%\n",
      "Epoch [107/200], Loss: 0.7709, Train Acc: 61.07%, Test Acc: 59.29%\n",
      "Epoch [108/200], Loss: 0.6870, Train Acc: 60.27%, Test Acc: 58.62%\n",
      "Epoch [109/200], Loss: 0.7768, Train Acc: 61.27%, Test Acc: 59.79%\n",
      "Epoch [110/200], Loss: 0.8644, Train Acc: 61.50%, Test Acc: 58.21%\n",
      "Epoch [111/200], Loss: 0.6913, Train Acc: 61.25%, Test Acc: 60.00%\n",
      "Epoch [112/200], Loss: 0.9657, Train Acc: 60.88%, Test Acc: 59.88%\n",
      "Epoch [113/200], Loss: 0.6240, Train Acc: 60.59%, Test Acc: 59.62%\n",
      "Epoch [114/200], Loss: 1.1907, Train Acc: 60.27%, Test Acc: 59.92%\n",
      "Epoch [115/200], Loss: 0.8135, Train Acc: 61.75%, Test Acc: 58.83%\n",
      "Epoch [116/200], Loss: 0.7819, Train Acc: 60.41%, Test Acc: 59.33%\n",
      "Epoch [117/200], Loss: 0.7111, Train Acc: 62.02%, Test Acc: 59.08%\n",
      "Epoch [118/200], Loss: 0.6048, Train Acc: 60.30%, Test Acc: 60.75%\n",
      "Epoch [119/200], Loss: 0.8561, Train Acc: 61.71%, Test Acc: 58.21%\n",
      "Epoch [120/200], Loss: 0.8256, Train Acc: 61.41%, Test Acc: 59.17%\n",
      "Epoch [121/200], Loss: 0.7275, Train Acc: 61.21%, Test Acc: 60.08%\n",
      "Epoch [122/200], Loss: 0.8711, Train Acc: 60.34%, Test Acc: 60.17%\n",
      "Epoch [123/200], Loss: 0.9063, Train Acc: 60.21%, Test Acc: 60.54%\n",
      "Epoch [124/200], Loss: 0.7029, Train Acc: 61.55%, Test Acc: 58.83%\n",
      "Epoch [125/200], Loss: 0.7640, Train Acc: 60.75%, Test Acc: 60.12%\n",
      "Epoch [126/200], Loss: 0.9921, Train Acc: 61.50%, Test Acc: 59.62%\n",
      "Epoch [127/200], Loss: 0.6639, Train Acc: 60.36%, Test Acc: 60.46%\n",
      "Epoch [128/200], Loss: 0.9665, Train Acc: 62.18%, Test Acc: 60.75%\n",
      "Epoch [129/200], Loss: 0.8670, Train Acc: 61.29%, Test Acc: 59.92%\n",
      "Epoch [130/200], Loss: 0.7766, Train Acc: 61.32%, Test Acc: 60.38%\n",
      "Epoch [131/200], Loss: 0.6374, Train Acc: 61.29%, Test Acc: 60.04%\n",
      "Epoch [132/200], Loss: 0.6183, Train Acc: 61.14%, Test Acc: 58.46%\n",
      "Epoch [133/200], Loss: 0.8270, Train Acc: 61.23%, Test Acc: 60.46%\n",
      "Epoch [134/200], Loss: 0.7806, Train Acc: 61.54%, Test Acc: 60.79%\n",
      "Epoch [135/200], Loss: 0.7036, Train Acc: 61.00%, Test Acc: 59.12%\n",
      "Epoch [136/200], Loss: 0.5528, Train Acc: 61.46%, Test Acc: 60.04%\n",
      "Epoch [137/200], Loss: 0.7823, Train Acc: 61.88%, Test Acc: 60.08%\n",
      "Epoch [138/200], Loss: 0.6102, Train Acc: 60.95%, Test Acc: 60.04%\n",
      "Epoch [139/200], Loss: 0.9349, Train Acc: 61.48%, Test Acc: 57.21%\n",
      "Epoch [140/200], Loss: 0.6411, Train Acc: 61.38%, Test Acc: 59.75%\n",
      "Epoch [141/200], Loss: 0.9369, Train Acc: 61.59%, Test Acc: 60.25%\n",
      "Epoch [142/200], Loss: 0.7560, Train Acc: 61.55%, Test Acc: 60.08%\n",
      "Epoch [143/200], Loss: 0.6909, Train Acc: 60.93%, Test Acc: 59.54%\n",
      "Epoch [144/200], Loss: 0.8552, Train Acc: 60.84%, Test Acc: 61.17%\n",
      "Epoch [145/200], Loss: 1.0158, Train Acc: 61.52%, Test Acc: 60.38%\n",
      "Epoch [146/200], Loss: 0.6800, Train Acc: 61.73%, Test Acc: 59.58%\n",
      "Epoch [147/200], Loss: 0.5560, Train Acc: 61.79%, Test Acc: 59.38%\n",
      "Epoch [148/200], Loss: 1.0182, Train Acc: 61.93%, Test Acc: 59.29%\n",
      "Epoch [149/200], Loss: 0.5295, Train Acc: 61.88%, Test Acc: 60.58%\n",
      "Epoch [150/200], Loss: 0.5682, Train Acc: 61.59%, Test Acc: 60.17%\n",
      "Epoch [151/200], Loss: 0.8239, Train Acc: 61.71%, Test Acc: 59.83%\n",
      "Epoch [152/200], Loss: 0.7922, Train Acc: 61.64%, Test Acc: 61.04%\n",
      "Epoch [153/200], Loss: 0.8577, Train Acc: 60.73%, Test Acc: 59.58%\n",
      "Epoch [154/200], Loss: 0.7083, Train Acc: 62.20%, Test Acc: 58.62%\n",
      "Epoch [155/200], Loss: 0.7951, Train Acc: 61.88%, Test Acc: 59.83%\n",
      "Epoch [156/200], Loss: 0.7949, Train Acc: 61.75%, Test Acc: 60.00%\n",
      "Epoch [157/200], Loss: 0.7736, Train Acc: 62.62%, Test Acc: 60.38%\n",
      "Epoch [158/200], Loss: 0.7058, Train Acc: 62.07%, Test Acc: 59.96%\n",
      "Epoch [159/200], Loss: 0.9229, Train Acc: 61.46%, Test Acc: 60.00%\n",
      "Epoch [160/200], Loss: 1.0505, Train Acc: 61.82%, Test Acc: 59.62%\n",
      "Epoch [161/200], Loss: 0.7749, Train Acc: 60.75%, Test Acc: 58.71%\n",
      "Epoch [162/200], Loss: 0.7416, Train Acc: 61.25%, Test Acc: 59.79%\n",
      "Epoch [163/200], Loss: 0.8283, Train Acc: 61.61%, Test Acc: 60.75%\n",
      "Epoch [164/200], Loss: 0.7626, Train Acc: 61.27%, Test Acc: 60.75%\n",
      "Epoch [165/200], Loss: 0.7864, Train Acc: 61.73%, Test Acc: 60.67%\n",
      "Epoch [166/200], Loss: 0.6485, Train Acc: 61.64%, Test Acc: 59.75%\n",
      "Epoch [167/200], Loss: 1.0881, Train Acc: 61.80%, Test Acc: 60.58%\n",
      "Epoch [168/200], Loss: 0.8636, Train Acc: 61.64%, Test Acc: 58.00%\n",
      "Epoch [169/200], Loss: 0.7853, Train Acc: 61.16%, Test Acc: 60.00%\n",
      "Epoch [170/200], Loss: 0.4765, Train Acc: 62.12%, Test Acc: 60.67%\n",
      "Epoch [171/200], Loss: 0.8595, Train Acc: 61.98%, Test Acc: 58.96%\n",
      "Epoch [172/200], Loss: 0.7395, Train Acc: 61.09%, Test Acc: 60.46%\n",
      "Epoch [173/200], Loss: 1.0010, Train Acc: 62.23%, Test Acc: 60.83%\n",
      "Epoch [174/200], Loss: 0.7309, Train Acc: 61.39%, Test Acc: 61.04%\n",
      "Epoch [175/200], Loss: 0.6464, Train Acc: 62.11%, Test Acc: 60.75%\n",
      "Epoch [176/200], Loss: 0.6296, Train Acc: 61.27%, Test Acc: 59.62%\n",
      "Epoch [177/200], Loss: 0.7124, Train Acc: 61.50%, Test Acc: 61.04%\n",
      "Epoch [178/200], Loss: 0.4838, Train Acc: 62.16%, Test Acc: 60.88%\n",
      "Epoch [179/200], Loss: 1.0109, Train Acc: 61.61%, Test Acc: 60.75%\n",
      "Epoch [180/200], Loss: 0.5420, Train Acc: 62.64%, Test Acc: 59.12%\n",
      "Epoch [181/200], Loss: 0.8215, Train Acc: 61.36%, Test Acc: 58.67%\n",
      "Epoch [182/200], Loss: 0.6806, Train Acc: 63.11%, Test Acc: 61.12%\n",
      "Epoch [183/200], Loss: 0.7591, Train Acc: 61.80%, Test Acc: 60.67%\n",
      "Epoch [184/200], Loss: 0.8790, Train Acc: 61.91%, Test Acc: 60.17%\n",
      "Epoch [185/200], Loss: 0.9028, Train Acc: 62.18%, Test Acc: 60.04%\n",
      "Epoch [186/200], Loss: 0.9124, Train Acc: 61.46%, Test Acc: 59.79%\n",
      "Epoch [187/200], Loss: 0.7588, Train Acc: 61.21%, Test Acc: 60.67%\n",
      "Epoch [188/200], Loss: 0.8496, Train Acc: 62.32%, Test Acc: 61.12%\n",
      "Epoch [189/200], Loss: 0.7305, Train Acc: 61.62%, Test Acc: 59.54%\n",
      "Epoch [190/200], Loss: 0.9186, Train Acc: 61.77%, Test Acc: 59.42%\n",
      "Epoch [191/200], Loss: 0.9231, Train Acc: 61.30%, Test Acc: 59.08%\n",
      "Epoch [192/200], Loss: 0.9034, Train Acc: 61.80%, Test Acc: 61.58%\n",
      "Epoch [193/200], Loss: 0.6503, Train Acc: 61.86%, Test Acc: 59.12%\n",
      "Epoch [194/200], Loss: 0.8846, Train Acc: 62.43%, Test Acc: 59.29%\n",
      "Epoch [195/200], Loss: 0.7044, Train Acc: 61.96%, Test Acc: 61.00%\n",
      "Epoch [196/200], Loss: 0.7984, Train Acc: 61.62%, Test Acc: 60.83%\n",
      "Epoch [197/200], Loss: 0.7071, Train Acc: 62.75%, Test Acc: 59.50%\n",
      "Epoch [198/200], Loss: 0.5601, Train Acc: 61.43%, Test Acc: 60.62%\n",
      "Epoch [199/200], Loss: 0.7826, Train Acc: 62.05%, Test Acc: 60.46%\n",
      "Epoch [200/200], Loss: 0.6968, Train Acc: 61.25%, Test Acc: 60.46%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "num_epochs = 200\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # 确保模型在训练模式\n",
    "    correct_train = 0\n",
    "    total_train = 0\n",
    "    for inputs, targets in train_loader:\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total_train += targets.size(0)\n",
    "        correct_train += (predicted == targets).sum().item()\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    # 计算训练准确率\n",
    "    train_accuracy = 100 * correct_train / total_train\n",
    "    train_acc.append(train_accuracy)\n",
    "    \n",
    "    # 测试模型\n",
    "    model.eval()  # 设置模型为评估模式\n",
    "    correct_test = 0\n",
    "    total_test = 0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in DataLoader(TensorDataset(X_test_tensor, y_test_tensor), batch_size=64):\n",
    "            outputs = model(inputs)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total_test += labels.size(0)\n",
    "            correct_test += (predicted == labels).sum().item()\n",
    "            all_preds.extend(predicted.numpy())\n",
    "            all_labels.extend(labels.numpy())\n",
    "\n",
    "    # 计算测试准确率\n",
    "    test_accuracy = 100 * correct_test / total_test\n",
    "    test_acc.append(test_accuracy)\n",
    "\n",
    "    # 每个epoch打印训练和测试的损失和准确率\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, Train Acc: {train_accuracy:.2f}%, Test Acc: {test_accuracy:.2f}%')\n",
    "\n",
    "# 绘制训练和测试准确率图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(range(1, num_epochs+1), train_acc, label='Train Accuracy')\n",
    "plt.plot(range(1, num_epochs+1), test_acc, label='Test Accuracy')\n",
    "plt.title('Accuracy over epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 计算混淆矩阵\n",
    "cm = confusion_matrix(all_labels, all_preds)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b063d632-7c52-485a-977d-5cd3d025fc20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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